The World Wide Web contains a wealth of opinions on just about anything. Online opinions come in various shapes and sizes, from short and informal talkback comments through opinionated blog postings to long and argumentative editorials. An important type of opinionated postings is the kind dedicated to product reviews in Internet forums. In this era of user-generated content, writing product reviews is a widespread activity. People's buying decisions are significantly influenced by such product reviews. However, in many cases particularly for popular products, the number of reviews may be large, which causes many reviews to be left unnoticed (there are thousands of reviews on the popular books). As a result, there is an increasing interest in opinion mining and review analysis, with the goal of automatically finding the most helpful reviews. In order to help users find the best reviews, some websites (e.g., amazon.com) employ a voting system in which users can vote for review helpfulness (“was this review helpful to you? yes/no”). However, user voting mechanisms suffer from various types of bias, including the imbalance vote bias (users tend to value others' opinions positively rather than negatively), the winner circle bias (reviews with many votes get more attention and therefore accumulate votes disproportionately), and the early bird bias (the first reviews to be published tend to get more votes) (Liu et al. [7]).
Analysis of product reviews typically involves several different aspects. Wiebe et al. [12] learn to identify opinionated documents (reviews) by assigning a subjectivity score to each document. Subjectivity scores are learnt from an annotated corpus. Polarity of sentiment is a well-studied aspect. For example, Pang et al., [9] compare different machine learning algorithms for sentiment classification of movie reviews. Turney [13] and Kushal et al. [14] classify reviews according to the polarity of the sentiment expressed. Turney [13] uses the average semantic orientation of the review in order to classify its polarity. The semantic orientation is calculated by mutual information between adverbs and adjectives in the review and the words ‘poor’ and ‘excellent’, while Kushal et al. [14] use WordNet and other heuristics for the same purpose. McDonald et al. [15] present a model for fine-to-coarse sentiment analysis from the sentence level to the review level and (Goldberg and Zhu [2]) use graph theory to learn review sentiment from a sparsely labeled corpus. Blitzer et al. [17] present an algorithm for domain adaptation of sentiment analysis.
Another area of interest in review analysis is review summarization, where (Hu and Liu, [4]) extract product features and output a sentiment-based summary-like list of product features and sentences that describe them. Popescu and Etzioni [10] use their KnowItAll system to improve upon (Hu and Liu [4]).
A few studies learn the quality of the reviews. Liu et al. [8] identify low quality reviews in order to improve the summarization of sentiment regarding product features. Kim et al. [5] predict the helpfulness of a review by structural features such as length, lexical features, and meta-data such as rating summary (star rating at amazon.com). Review subjectivity (where “the reviewer gives a very personal description of the product”) was used to predict helpfulness in (Ghose and Ipeirotis [1]).
Broadly taken, reader reviews can be thought of as essays with the target of the reviews as their topic. Off-topic (i.e. irrelevant) student essays were identified based on lexical similarity in (Higgins et al. [3]). From a different point of view, product reviews are opinions on the product stated from various perspectives. The different perspectives expressed in documents were distinguished based on their statistical distribution divergence in (Lin and Hauptmann [7]).
Kim et al. [5] used lexical features of three types: a version of the tf-idf measure, product features extracted from Pro/Con listings on epinions.com and sentiment words from publicly available lists. Their features also include metadata such as the stars rating.
In order to train a binary classifier that identifies poor quality reviews of electronic products, (Liu et al. [8]) employed four annotators, each annotator following very detailed instructions in the course of annotating thousands of reviews. Having thousands of reviews annotated and ranked by one evaluator might be problematic, since after a few dozen reviews it is hard for the evaluator to assess the true helpfulness of a review due to cognitive load by information learnt from previous reviews. It would therefore represent an improvement over the approach proposed by Liu et al. [8] to provide a fully unsupervised review that avoids the use of pre-made lists of sentiment words and other features altogether. It would also represent an improvement to obviate the need for preprocessing proposed by Popescu and Etzioni [10] who use parsers, NER systems and POS taggers in a preprocessing stage. Avoiding preprocessing systems is a clear advantage since these systems are usually trained on well-written corpora, and thus tend to perform poorly on freely-formed user generated content such as book reviews.
US20020069190 discloses a method for ranking a set of documents, comprising the steps of: gathering context information from the documents; generating at least one rank criterion from the context information; and ranking the documents, based on the at least one rank criterion. The method is based on loop-back or feedback provided by user re-evaluation of the dominant concepts.
WO0146821A 1 discloses a computer program that indicates lexical impact of various words in a text and provides various statistics relating to lexical impact of the text. Also, a ranked thesaurus for listing alternative words (e.g., synonyms, antonyms, related), along with an indication of their relative lexical impact. The thesaurus may alternatively rank words according to ranking system.